legacy-datasets/banking77
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How to use happytree09/distilbert-base-banking77-pt2 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-classification", model="happytree09/distilbert-base-banking77-pt2") # Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("happytree09/distilbert-base-banking77-pt2")
model = AutoModelForSequenceClassification.from_pretrained("happytree09/distilbert-base-banking77-pt2")This model is a fine-tuned version of distilbert-base-uncased on the banking77 dataset. It achieves the following results on the evaluation set:
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The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 1.0001 | 1.0 | 626 | 0.7166 | 0.8399 |
| 0.363 | 2.0 | 1252 | 0.3527 | 0.9129 |
| 0.1849 | 3.0 | 1878 | 0.3063 | 0.9239 |